🧠 Conversational DB Agent

Chat with your MongoDB database using natural language queries!

- This AI-powered agent understands user queries from natural language

- Convert user queries into MongoDB queries to extract requiered data from database

- Uses **Agentic Reasoning** via CrewAI to return insightful results 

- Loom Video to show working model - https://www.loom.com/share/8565bcc353994287a404885fcaf1db5b?sid=b8845cfc-d1fc-4a04-8c5e-c948cec09dad

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🚀 Features

• 💬 Chat interface using Streamlit
• 🧠 Intent matching via semantic similarity search using FAISS.
• 🔌 Connects to MongoDB collections (accounts, transactions, customers from sample_analytics database)
• 🤖 Agent-based architecture using LangChain
• 📚 Sample question dataset for guided queries
• 🔎 Tools for structured DB retrieval: get customer tiers, accounts, transactions, etc.
• Handle Multiple database operationsin one conversation
• Error Handling for ambiguous/ impossible queries

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⚙️ Setup Instructions

1. Clone the Repository

git clone https://github.com/your-username/conversational-db-agent.git
cd conversational-db-agent

2. Install Dependencies

We recommend using a virtual environment:

python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

pip install -r requirements.txt

3. Configure Environment Variables

Create a .env file in the root directory:

MONGO_URI="your-mongodb-uri"
GOOGLE_API_KEY="your-generative-ai-key"

4. Run the App

streamlit run app.py

The app will open in your browser at http://localhost:8501.

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🧠 Conversational Database Agent - Architecture

• The system allows users to query a MongoDB database using natural language, powered by LangChain Agent, Google Generative AI Chat Model, and Streamlit for an interactive UI.

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📌 High-Level Overview

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🧱 Components

1. Streamlit Frontend (app.py)

• Provides a chat-based user interface.
• Displays both user queries and agent responses.
• Handles session management and chat history.

2. LangChain Agent (main.py)

• Central logic for handling natural language input.
• Uses LangChain tools and memory for interactive querying.
• Integrates a custom toolset for MongoDB operations.

3. Tools

Functions registered with the LangChain agent to perform specific DB operations:

• get_customer_tiers
• get_customers_with_email_domain
• get_accounts_for_username
• And many more defined in main.py.

4. FAISS Vectorstore

• Performs similarity search to identify user intent.
• Matches user input with stored intents from sample_questions.json.

5. MongoDB Database

• Primary data source (MongoDB Atlas or local).
• Collections: customers, accounts, transactions.
• Link for dataset - https://www.mongodb.com/docs/atlas/sample-data/sample-analytics/

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🔁 Data Flow

1. User enters natural language query in Streamlit chat.
2. app.py sends the query to the agent (agent.run(user_input)).
3. Vectorstore checks the query against sample_questions.json for intent matching using FAISS.
4. LangChain Agent:
   • Determines the best matching tool or route.
   • Executes the appropriate function (Tool).
5. Tool executes MongoDB query using pymongo.
6. Extracted data from databse analyzed by Agent and outputs structured response for user query in natural language.
7. Response is returned from Agent → Streamlit.
8. User sees the assistant's response in the chat.

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🧠 Technologies Used

Component	Technology
UI	Streamlit
LLM Integration	LangChain + Google GenAI
Vector Database	FAISS
Backend Logic	Python + LangChain Tools
Database	MongoDB (via PyMongo)

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🗂 Directory Structure

├── app.py                      # Streamlit frontend app
├── main.py                     # Core agent logic and tools
├── .env                        # MongoDB URI KEY and LLM API KEY
├── requirements.txt            # Python dependencies
├── sample_questions.json       # Pre-defined sample questions for few shot learning
├── README.md                   # Project documentation (you are here)

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🔐 Environment Variables

Stored in a .env file:

• MONGO_URI=your_mongodb_connection_string
• GOOGLE_API_KEY=your_google_genai_api_key

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💡 Sample Questions

Sample questions are loaded from sample_questions.json to help agent to analyze different types of user’s query intent. Examples:

• "Show me transactions greater than 1000 for account 328304."
• "List customer tiers and their benefits."
• "Retrieve all customers with 'xyz@gmail.com' email."

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